IIMYC   23581
INSTITUTO DE INVESTIGACIONES MARINAS Y COSTERAS
Unidad Ejecutora - UE
artículos
Título:
From town to town: predicting the taxonomic, functional and phylogenetic diversity of birds using NDVI
Autor/es:
BELLOCQ ISABEL; LEVEAU LUCAS; ISLA, FEDERICO IGNACIO
Revista:
ECOLOGICAL INDICATORS
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2020 vol. 1067 p. 1 - 11
ISSN:
1470-160X
Resumen:
Biodiversity mapping in urban areas is imperative for their conservation. Remote sensors produce environmental information, such as the Normalized Difference Vegetation Index (NDVI), an indicator of vegetation cover in urban areas. NDVI can be used to predict the taxonomic, functional and phylogenetic bird diversity in urban areas. Moreover, a predictive model constructed in a city can be used to predict the bird diversity in other cities. The objectives of this study were: 1) to construct and evaluate predictive models between NDVI and taxonomic, functional and phylogenetic diversity of birds in Mar del Plata city, Argentina; and 2) to extrapolate this model to two other cities in the region: Balcarce and Miramar. Generalized additive models were applied to relate bird diversity variations to NDVI. In Mar del Plata, the taxonomic and functional diversity increased with increasing NDVI values, and the predictive models explained 64-81% of the taxonomic and functional diversity variation. The models correctly predicted diversity values in additional transects not included in the model, although they had a low predictive power of phylogenetic diversity variation. The models constructed in Mar del Plata adequately predicted the spatial variation of species diversity (Shannon index) in Balcarce and Miramar, the spatial variation of species richness in Balcarce, and the variation of functional diversity in Miramar. The use of NDVI to construct urban predictive models has been successful to simulate maps of different bird diversity facets, whereas the extrapolation to other cities was more useful to predict taxonomic diversity.